## Jun Chen - ResearchMy research interests focus on the design, analysis and implementation of control and optimization algorithms for large-scale autonomous systems. More specifically, I research novel decision-making algorithms and use these algorithms to build planning modules that enable complex systems to operate autonomously safely and efficiently. Motivated by the goal of building practical methodology for robust real-time decision-making, a research program is built for computational control and optimization, which could provide complex systems reliable, accurate, computational efficient and robust solutions. ## Research SponsorsI gratefully acknowledge research support from TuSimple, MIT Lincoln Laboratory and Federal Aviation Administration. ## Real-time Decision-making Algorithms and Tools for Autonomous SystemsReal-time decision-making is critical for the autonomous systems due to their dynamic nature. An efficient computational framework with the proper model is the key to help deliver real-time solutions for autonomous system. In particular, a faster computational platform can solve larger problems in the same time and solve the same problem more often in face of disruptions. The research on efficient stochastic control and optimization will focus on two aspects: For the modeling side, we want to expand classes of problems for which one can generate approximation algorithms, which can be decomposed into massive subproblems to solve in parallel and efficiently. For the computing side, we want to focus the work in designing practical framework based on the Fog Computing, which distributes computation, communication, control and storage closer to the end users. For instance, the distributed computing system based on connected mobile devices could perform massive parallel computing.
## Large Scale Stochastic OptimizationThis line of research roots in the fundamental mathematical modeling and algorithms designing based on ## Fog Computing for Connected Large Scale Systems
Fog framework takes the advantage of a collaborative multitude of end-user devices to perform parallel computing locally. Our algorithms identifies specific mathematical format, which enables us to decompose the original large-scale problem into massive subproblems. The decentralized optimization framework can automatically distribute the sub-problems to the available nodes in the network such that the computing efficiency is greatly improved through massive distributed computing. ## UAVs Traffic Management (UTM)As the UAV systems are actively integrated into our current national airspace system, the possibility of deployment of large groups of UAV closely cooperating together brings new potentialities for autonomous systems. The goal of ## Learning-Based Control for Autonomous SystemsThis research focuses on combining the optimization and modeling techniques from the |